Knowledge that Transforms

Sovereign debt yields have undergone a historic decline over the last half century. Standard explanations, including aging populations and increases in asset demand from abroad, encounter difficulties when confronted with the full range of evidence. We propose an explanation based on a decline in inflation and default risk. We show that a model with sovereign default captures the decline in interest rates, the stability of equity valuation ratios, and the reduction in investment and output growth. Calibrations of the model post-COVID suggest that sovereign default risk may have returned.

We measure the nature and severity of a variety of belief distortions in market reactions to hundreds of economic news events by synthesizing structural estimation with algorithmic machine learning to quantify bias. We find that investors systematically overreact to perceptions about multiple fundamental shocks, a phenomenon we show often dampens rather than amplifies market volatility via a “shock composition effect.” Such effects imply that the stock market can underreact to news, even when investors overreact to all shocks.

Government intervention in the financial market through its own trading fundamentally changes the market’s structure, function, and outcome. I develop a general equilibrium framework to study the impact of government trading on investor welfare. I show that with incompleteness and information asymmetry, the market equilibrium is in general suboptimal, and government intervention can potentially improve investor welfare even with no additional information. However, the actual welfare impact of government intervention is sensitive to the details of the economy and the policy design. Popular policy targets such as price stability, market liquidity, and informational efficiency can have different welfare implications.

We show that U.S. firms cut imports by 31.8% when their international suppliers experience environmental and social (E&S) incidents. These trade cuts are larger for publicly listed U.S. importers facing high E&S investor pressure and lead to cross-country supplier reallocation, suggesting that E&S preferences in capital markets can be privately costly but have real effects for foreign suppliers. Larger trade cuts around the incident result in better supplier E&S performance in subsequent years and in the eventual resumption of trade. Our results highlight the role of investors in ensuring suppliers’ E&S compliance along global supply chains.

Traditionally, fund managers cast votes on behalf of fund investors. Recently, there is a shift toward “pass-through voting,” with funds offering investors a choice: delegate votes to the fund or vote themselves. We develop a framework to study the implications of voting choice. While it helps reflect heterogeneous investor preferences, it also shapes the informational content of the vote, and these forces can conflict. When interests are aligned, voting choice improves information aggregation and investor welfare. With preference heterogeneity or costly information, however, it can make investors worse off by weakening informed.

Generative artificial intelligence (AI) is increasingly consuming and producing huge amounts of data. We propose a social learning model of AI, emphasizing a data-AI feedback loop: data quality affects AI productivity, which influences AI adoption and, consequently, the composition (AI versus human-generated) and quality of future data. Calibrated to evidence on synthetic training loops, the model predicts hump-shaped labor dynamics—short-term displacement that partially reverses as data quality deteriorates. A Grossman–Stiglitz-style externality emerges: AI adopters free-ride on the human-generated actions that supply the novel information on which AI itself relies. In a competitive market, AI should be taxed to correct the data-quality externality; a concentrated AI industry overcorrects, making a subsidy optimal.

There are two major institutional investors in the syndicated loan market: collateralized loan obligations (CLOs) and loan mutual funds. CLOs are closed-end funds while loan funds are open-end funds that issue claims that are redeemable on demand. In this paper, we examine whether CLOs provide arbitrage capital that contributes to the resilience of loan funds. We find that CLOs act as shock absorbers, providing liquidity through par-building trades when loan funds experience large outflows. However, CLO-provided liquidity is limited to par build–eligible loans, leading to potential flow-induced fire sales among par build–ineligible loans. (JEL G23, G38

We show that positive flows to active mutual funds with high recent returns partially reverse at longer horizons. This outcome is robust across a broad range of alternative specifications. Reversal occurs from greater outflows associated with high prior returns, not reduced inflows. We test theories to explain the reversal: investment life cycles, tax loss selling, and a behavioral “disappointment” hypothesis based on investors’ overreaction to positive returns. While both tax loss selling and short investor life cycles can contribute, the evidence supports a role for investor disappointment, whereby investors redeem their capital when return performance fails to meet expectations.

A fundamental concern about green investing is that it may crowd out political support for public policies addressing negative externalities. We examine this concern in a preregistered experiment conducted shortly before a real referendum on a climate law in Switzerland. We find that offering an opportunity to invest in a climate-friendly fund does not reduce individual support for climate regulation, measured by political donations and voting intentions. A replication of the experiment in the United Kingdom yields similar results. Our estimates reject a crowding-out effect, suggesting instead a modest crowding-in effect of green investing on political support for green policies.

We develop a model-free measure of the variance premium by constructing option portfolios whose returns are highly correlated with realized stock variance. This effectively decomposes returns into realized variance minus implied variance. We apply this decomposition to document a novel quarterly cross-sectional continuation pattern in both realized variance and implied variance of individual stocks. Implied variance underanticipates the seasonality of realized variance, so options that performed well at quarterly lags continue to earn high returns in the future. Quarterly periodicity in realized stock variance only occurs on days with analyst earning revisions, suggesting an informational channel for this pattern.